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Improving Vehicle Trip Generation Estimations for Urban Contexts: A Method Using Household Travel Surveys to Adjust ITE Trip Generation Rates

機(jī)譯：改善城市環(huán)境的車(chē)輛出行估計(jì)：一種利用家庭旅行調(diào)查來(lái)調(diào)整ITE出行率的方法

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The purpose of this research is to develop and test a widely available, ready-to-use method for adjusting the Institute of Transportation Engineers (ITE) Trip Generation Handbook vehicle trip generation estimates for urban context using regional household travel survey data. The ITE Handbook has become the predominant method for estimating vehicle trips generated by different land uses or establishment, providing a method for data collection and vehicle trip estimation based on the size of the development (e.g. gross square footage, number of employees, number of dwelling units). These estimates are used in traffic impact analysis to assess the amount of impact the development will have on nearby transportation facilities and, the corresponding charges for mitigating the developmentu27s negative impacts, with roadway expansions, added turning bays, additional parking or traffic signalization, for example.The Handbook is often criticized, however, for its inability to account for variations in travel modes across urban contexts. For more than fifty years, ITE has collected suburban, vehicle-oriented data on trip generation for automobiles only. Despite the provision of warnings against application in urban areas, local governments continue to require the use of the ITE Handbook across all area-types. By over predicting vehicle traffic to developments in urban developments, developments may be overcharged to mitigate these developments locating in urban environments despite the lower automobile mode shares, discouraging infill development or densification. When ITEu27s Trip Generation Handbook overestimates the vehicle impact of a development, facilities are also overbuilt for the automobile traffic and diminishing the use of alternative modes. When ITEu27s TGH underestimates this impact, adjacent facilities may become oversaturated with traffic, pushing cars onto smaller facilities nearby. Currently, there is momentum amongst practitioners to improve these estimation techniques in urban contexts to help support smart growth and better plan for multiple modes.This research developed and tested a method to adjust ITEu27s Handbook vehicle trip generation estimates for changes in transportation mode shares in more urban contexts using information from household travel surveys. Mode share adjustments provide direct reductions to ITEu27s Handbook vehicle trip estimations. Household travel survey (HTS) data from three regions were collected: Portland, Oregon; Seattle, Washington; and Baltimore, Maryland. These data were used to estimate the automobile mode share rates across urban context using three different adjustment methodologies: (A) a descriptive table of mode shares across activity density ranges, (B) a binary logistic regression that includes a built environment description of urban context with the best predictive power, and (C) a binary logistic regression that includes a built environment description of urban context with high predictive power and land use policy-sensitivity. Each of these three methods for estimating the automobile mode share across urban context were estimated for each of nine land use categories, resulting in nine descriptive tables (Adjustment A) and eighteen regressions (Adjustments B and C). Additionally, a linear regression was estimated to predict vehicle occupancy rates across urban contexts for each of nine land use categories.195 independently collected establishment-level vehicle trip generation data were collected in accordance with the ITE Handbook to validate and compare the performance of the three adjustment methods and estimations from the Handbook. Six land use categories (out of the nine estimated) were able to be tested. Out of all of the land uses tested and verified, ITEu27s Trip Generation Handbook appeared to have more accurate estimations for land uses that included residential condominiums/townhouses (LUC 230), supermarkets (LUC 850) and quality (sit-down) restaurants (LUC 931). Moderate or small improvements were observed when applying urban context adjustments to mid-rise apartments (LUC 223), high-turnover (sit-down) restaurants (LUC 932). The most substantial improvements occurred at high-rise apartments (LUC 222) and condominiums/townhouses (LUC 232), shopping centers (LUC 820), or coffee/donut (LUC 936) or bread/donut/bagel shops (LUC 939) without drive-through windows. The three methods proposed to estimate automobile mode share provides improvements to the Handbook rates for most infill developments in urban environments.For the land uses analyzed, it appeared a descriptive table of mode shares across activity density provided results with comparable improvements to the results from the more sophisticated binary logistic model estimations. Additional independently collected establishment-level data collections representing more land uses, time periods and time of days are necessary to determine how ITEu27s Handbook performs in other circumstances, including assessing the transferability of the vehicle trip end rates or mode share reductions across regions.
機(jī)譯：這項(xiàng)研究的目的是開(kāi)發(fā)和測(cè)試一種廣泛使用的，可立即使用的方法，用于使用區(qū)域性家庭旅行調(diào)查數(shù)據(jù)來(lái)調(diào)整城市環(huán)境下的運(yùn)輸工程師協(xié)會(huì)（ITE）出行記錄手冊(cè)的出行記錄估計(jì)。 ITE手冊(cè)已成為估算由不同土地用途或場(chǎng)所產(chǎn)生的車(chē)輛出行的主要方法，它提供了一種根據(jù)開(kāi)發(fā)規(guī)模（例如，總平方英尺，員工人數(shù)，住房數(shù)量）進(jìn)行數(shù)據(jù)收集和車(chē)輛出行估計(jì)的方法單位）。這些估算值將用于交通影響分析中，以評(píng)估開(kāi)發(fā)項(xiàng)目對(duì)附近交通設(shè)施的影響程度，以及用于減輕開(kāi)發(fā)項(xiàng)目的負(fù)面影響的相應(yīng)費(fèi)用，包括道路擴(kuò)展，增加的轉(zhuǎn)彎處，額外的停車(chē)位或交通信號(hào)燈，但是，由于手冊(cè)無(wú)法解釋整個(gè)城市環(huán)境中出行方式的變化，因此經(jīng)常受到批評(píng)。五十多年來(lái)，ITE僅收集了郊區(qū)的，以車(chē)輛為導(dǎo)向的有關(guān)旅行產(chǎn)生的數(shù)據(jù)。盡管提供了禁止在城市地區(qū)應(yīng)用的警告，但地方政府仍然要求在所有區(qū)域類(lèi)型中使用ITE手冊(cè)。盡管汽車(chē)模式所占份額較低，但通過(guò)過(guò)度預(yù)測(cè)車(chē)輛交通到城市發(fā)展中的發(fā)展情況，可能會(huì)多收取一些開(kāi)發(fā)費(fèi)用，以緩解這些位于城市環(huán)境中的發(fā)展情況，從而不利于填充發(fā)展或致密化。當(dāng)ITEs的《旅行生成手冊(cè)》高估了開(kāi)發(fā)項(xiàng)目對(duì)車(chē)輛的影響時(shí)，設(shè)施也為汽車(chē)交通而過(guò)度建造，從而減少了替代模式的使用。當(dāng)ITE的TGH低估了這種影響時(shí)，鄰近的設(shè)施可能會(huì)變得交通擁擠，將汽車(chē)推向附近的較小設(shè)施。當(dāng)前，從業(yè)人員正積極改進(jìn)城市環(huán)境中的這些估算技術(shù)，以幫助支持智能增長(zhǎng)并更好地規(guī)劃多種模式。這項(xiàng)研究開(kāi)發(fā)并測(cè)試了一種方法，該方法可針對(duì)交通模式份額的變化調(diào)整ITE手冊(cè)的車(chē)輛出行生成估算在更多城市環(huán)境中使用家庭旅行調(diào)查的信息。模式份額調(diào)整可直接減少I(mǎi)TE u27s手冊(cè)的車(chē)輛行駛估計(jì)。收集了來(lái)自三個(gè)地區(qū)的家庭旅行調(diào)查（HTS）數(shù)據(jù)：俄勒岡州波特蘭市；華盛頓西雅圖；和馬里蘭州的巴爾的摩。這些數(shù)據(jù)用于通過(guò)三種不同的調(diào)整方法來(lái)估算城市環(huán)境中的汽車(chē)模式份額：（A）跨活動(dòng)密度范圍的模式份額描述表，（B）包括城市環(huán)境的已構(gòu)建環(huán)境描述的二進(jìn)制邏輯回歸具有最佳的預(yù)測(cè)能力，以及（C）二進(jìn)制logistic回歸，其中包括對(duì)城市環(huán)境的構(gòu)建環(huán)境描述，具有較高的預(yù)測(cè)能力和土地使用政策敏感性。針對(duì)九種土地利用類(lèi)別中的每一種，估計(jì)了三種估算城市環(huán)境中汽車(chē)模式份額的方法，每種方法都得出了九個(gè)描述性表（調(diào)整A）和十八個(gè)回歸表（調(diào)整B和C）。此外，估計(jì)線性回歸可預(yù)測(cè)九種土地利用類(lèi)別中每種城市環(huán)境下的車(chē)輛占用率。根據(jù)ITE手冊(cè)收集了195個(gè)獨(dú)立收集的企業(yè)級(jí)車(chē)輛出行生成數(shù)據(jù)，以驗(yàn)證和比較這三種性能調(diào)整方法和手冊(cè)中的估計(jì)?？梢詼y(cè)試六種土地利用類(lèi)別（估計(jì)的九種土地類(lèi)別）。在經(jīng)過(guò)測(cè)試和驗(yàn)證的所有土地用途中，ITE u27的《旅行生成手冊(cè)》似乎對(duì)土地用途進(jìn)行了更準(zhǔn)確的估算，包括住宅公寓/排屋（LUC 230），超級(jí)市場(chǎng)（LUC 850）和優(yōu)質(zhì)（坐下）餐館（LUC 931）。將城市環(huán)境調(diào)整應(yīng)用于中層公寓（LUC 223），高周轉(zhuǎn)率（sit-down）餐廳（LUC 932）時(shí)，觀察到中等或較小的改善。最顯著的改善發(fā)生在高層公寓（LUC 222）和公寓/排屋（LUC 232），購(gòu)物中心（LUC 820）或咖啡/甜甜圈（LUC 936）或面包/甜甜圈/百吉餅店（LUC 939）直通車(chē)窗。提議的三種估算汽車(chē)模式份額的方法可以改善城市環(huán)境中大多數(shù)填充物開(kāi)發(fā)的《手冊(cè)》比率。對(duì)于分析的土地用途，它提供了一個(gè)描述性表格，涵蓋了整個(gè)活動(dòng)密度下的模式份額，并提供了與之相比可比的改進(jìn)結(jié)果。更復(fù)雜的二進(jìn)制邏輯模型估計(jì)。額外的獨(dú)立收集的企業(yè)級(jí)數(shù)據(jù)收集，代表更多的土地用途，時(shí)間和時(shí)間是確定ITE u27s手冊(cè)在其他情況下的性能所必需的，包括評(píng)估跨區(qū)域的車(chē)輛出行結(jié)束率的可轉(zhuǎn)移性或模式份額的減少。

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    Currans Kristina Marie;

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  • 年度 2013
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